Improvement of a Daily Precipitation Stochastic Model Using the North Atlantic Oscillation Index

The North Atlantic Oscillation (NAO) index has a significant influence on weather anomalies and climate variability in Western Europe, having been well documented their influence in southwestern Europe causing low rainfall due to the positive phase of the NAO. In this work, we have studied the effect of making a seasonal perturbation in the second order-mixed exponential stochastic model (MC2ME) of daily precipitation using records of 54 years of daily precipitation data (1953-2006) in 33 weather stations located in the valley of the Guadalquivir, southern Spain. To do this, the procedure will be to perturb the parameters of the MC2ME model using a linear equation that includes the NAO index and a lag, so that the value of a model coefficient in current day is related to the value of the NAO index occurred several days, weeks or months earlier. The improvement of perturbing each of the twelve months of the year, independently, will be compared with the result obtained doing an annual perturbation. The seasonal study reveals that the months in which the NAO index has the greatest impact on improving the model are from November to March, the predominantly rainy months. If only the months where the influence of the NAO index is positive are perturbed, the value of log likelihood function improves relative to the value achieved when all the months of the year are perturbed. The lag found was predominantly zero days.